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Application Research of Vertical Federated Learning technology in Banking Risk Control Model strategy 21

Application Research of Vertical Federated Learning Technolo...

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21st IEEE international Symposium on Parallel and distributed Processing with Applications, 13th IEEE international conference on Big Data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Luo, Yong Lu, Zhi Yin, Xiaofei Lu, Songfeng Weng, Yiting Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan China China Minsheng Banking Corp. Ltd. Beijing China Shenzhen Huazhong University of Science and Technology Research Institute Wuhan China Shu Lun Pan Beijing China

ISBN: (纸本)9798350329223

This study centers on the application of vertical federated learning technology in the context of internet banking loans, with a particular focus on innovations in data privacy protection, risk control model algorithms, and secure multi-party computation. Currently, banking risk control strategies mainly rely on traditional data processing technologies, which often fall short in protecting user privacy and ensuring data usage efficiency. We adopt vertical federated learning technology, offering an innovative solution for the internet banking loan scenario. Firstly, regarding data privacy protection, we propose a differential privacy mechanism to safeguard user-sensitive data. Secondly, we innovatively apply risk control model algorithms, facilitating collaborative modeling across multiple internet loan platforms through federated learning. Furthermore, we introduce secure multi-party computation technology to ensure the secure transmission of data and confidentiality of computation processes during federated learning. Through empirical experiments on real internet loan datasets, we validate the effectiveness and feasibility of our proposed methods. After implementing our risk control model, the credit approval rate increased from 3.44% to 18.2%, with a single-day high reaching 25.53%. The average loan amount increased by 7,700 yuan, and the average interest rate slightly declined by 0.48%, marking a significant improvement and breakthrough compared to traditional risk control models. This study offers innovative solutions for data privacy protection and risk control in the internet loan scenario, providing safer and more reliable services for financial institutions and users. Moreover, our methods possess high practicality and promotional value. The potential widespread impact on the industry is profound. © 2023 IEEE.

关键词： Risk management

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A Comparative study on Cloud computing, Edge computing and Fog computing 1

A Comparative Study on Cloud Computing, Edge Computing and F...

引用

1st international conference on Recent Developments in Electronics and Communication Systems, RDECS 2022

作者： Tank, Birju Gandhi, Vaibhav Gujarat Technological University India B H GARDI COLLEGE of ENGINEERIGN & TECHNOLOGY India

ISBN: (纸本)9781643683607

In the recent era, Cloud computing has emerged as a high demanding technology. Apart from the cloud computing, edge and fog computing also becoming popular. Cloud computing was developed to provide steady and expansible services to end-users as well as industries. The merger of cloud computing with smart devices brings us into a new version of computing. Presently, Edge computing and fog computing techniques are flattering the world after cloud computing, which has all matured with the limitations in cloud. The objective of this study is to examine the current trends and concepts in detail, also, to provide a comparative study for the fog computing, edge computing, and cloud computing. This study includes few features, application, advantages as well as disadvantages, and the contrast between these technologies and how every one of them is effective for different applications. © 2023 The authors and IOS Press.

关键词： internet of things

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Intrusion Detection System in Explainable Artificial Intelligence by Using Different Algorithms 2

Intrusion Detection System in Explainable Artificial Intelli...

引用

2nd international conference on distributed computing and Optimization Techniques, ICDCOT 2024

作者： Satyanarayana, D. Saikiran, E. University Computer Science and Engineering Hyderabad India Santhiram Engineering College Nandyal India

ISBN: (纸本)9798350382952

The internet of Things (IoT) is a rapidly established technology that combines various domains and such technology permits devices to process, transfer, and receive information without the involvement of humans. However, privacy and security problems remain a major difficulty in the IoT. An Intrusion Detection System (IDS) is needed for securing attacks on this platform. Recently, various researchers identified significant ways for ID by employing Explainable Artificial Intelligence (XAI) approaches to Machine Learning (ML) and Deep Learning (DL) techniques. This research indicates various methodologies like Balanced and stacked Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), Extreme Gradient Boosting (XGB), Naïve Bayes (NB), Ada-Boost (AB), Cat-Boost (CB), Long Short-Term Memory (LstM), Deep Neural Network (DNN), and Bidirectional-LstM (Bi-LstM) that are utilized for IDS. Accuracy, f1-score, recall, Area Under Curve (AUC), precision, score time, and False Alarm Rate (FAR) are employed as the performance metrics for this study. © 2024 IEEE.

关键词： internet of things

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Dual Blockchain Framework for Halal Food Safety and Tracebility Concern With internet of Things 1

Dual Blockchain Framework for Halal Food Safety and Tracebil...

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1st international conference on technology, Engineering, and computing Applications, ICTECA 2023

作者： Adhiwibowo, W. Widayat, W. Syafei, W.A. Diponegoro University Department of Information Systems Semarang50275 Indonesia University Semarang Departemen of Informatics Engineering Indonesia

ISBN: (纸本)9798350360554

The need for animal products requires supervision and protection of national needs, especially for Muslims in Indonesia, who make up the majority of the population. Livestock products are a serious concern for countries where the majority of the population is Muslim because they prioritize the consumption of quality and halal food products. At the same time, the challenges faced include a series of product fraud incidents, accidental contamination, and the presence of residues in products. This can result in a loss of market share, reduced service levels, customer satisfaction, and a loss of reputation for local livestock meat products. In the context of the broader food trade system, the tasks associated with food delivery and supply chain reform in response to food safety issues are characterized by high costs, significant time requirements, and considerable complexity. Tracing of farmed meat products is required to collect, store, and transmit comprehensive records of livestock products throughout the Halal Food Supply Chain. The designed blockchain framework can be used to understand the processes in the Halal meat product supply chain. A block chain framework is a method for collecting data from multiple nodes, storing it in smart contracts, and managing it. This article explains the blockchain framework for distributing halal meat from slaughterhouses to consumers through traditional markets to meet the needs of the Indonesian Muslim community. © 2023 IEEE.

关键词： Blockchain

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A Systematic Review of Federated Learning Mechanism for The internet of Medical Things 1

A Systematic Review of Federated Learning Mechanism for The ...

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1st international conference on computing, Sciences and Communications, ICCSC 2024

作者： Aravind, P. Jerlin, M Asha Devarajan, Ganesh Gopal Nagarajan, Senthil Murugan School of Computer Science and Engineering Vellore Institute of Technology Chennai India SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Ghaziabad201204 India Medicine University of Luxembourg Faculty of Science Technology Department of Mathematics Luxembourg

ISBN: (纸本)9798350353648

The rapid evolution of healthcare technology, no-tably the integration of the internet of Medical Things (loMT), has revolutionized patient care, diagnosis, and treatment method-ologies. However, this progress introduces substantial challenges, primarily concerning the security, privacy, and interoperability of sensitive medical data. Federated Learning, a decentralized ma-chine learning approach, emerges as a possible solution to address these difficulties since it allows for collaborative model training without requiring raw patient data exchange. This systematic review explores and evaluates various FL mechanisms tailored for 10MT applications. The review examines existing literature, encompassing the amalgamation of loT and healthcare, the challenges faced in traditional health care systems, and the role of FL in mitigating these challenges. It highlights applications, privacy-preserving techniques, security measures, advantages, limitations, and potential enhancements in utilizing FL within 10MT environments. The review also presents a structured methodology for evaluating FL paradigms, encompassing performance metrics, privacy/security analysis, advantages/limitations assessment, and comparative analysis. Moreover, the paper pro-poses an innovative framework for federated learning in 10MT, detailing its architecture, modules, algorithms, functionalities, ethical considerations, and expected impact. Through a comprehensive analysis and synthesis of existing literature and proposed strategies, this review aims to provide insights for health care stakeholders in selecting and implementing optimal FL strategies for collaborative medical advancements while upholding stringent data privacy and security standards in 10MT. © 2024 IEEE.

关键词： Federated learning

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internet of Things System for Melon/Watermelon Plant Growth with Image-Based Fruit Weight Prediction 1

Internet of Things System for Melon/Watermelon Plant Growth ...

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1st international conference of Adisutjipto on Aerospace Electrical Engineering and Informatics, ICAAEEI 2024

作者： Sarosa, Moechammad Wirayoga, Septriandi Syaifudin, Yan Watequlis Fiernaningsih, Nilawati State Polytechnic of Malang Depart. of Electrical Engineering Malang Indonesia State Polytechnic of Malang Information Engineering Departement Malang Indonesia State Polytechnic of Malang Business Administration Departement Malang Indonesia

ISBN: (纸本)9798331507930

This research develops an internet of Things (IoT) system for watering automation and growth monitoring of melon and watermelon plants, with image processing-based fruit weight prediction. Through sensors of soil moisture, temperature, air humidity, and light intensity, the system automatically regulates watering based on environmental conditions, so that plants get water efficiently and in a timely manner. In addition, the system is equipped with a camera used to take pictures of fruits, which are then analyzed using image processing algorithms to automatically predict fruit weight. This research integrates IoT, image processing, and cloud computing technologies in one connected system, thus providing a comprehensive solution for irrigation management and crop monitoring. It is expected that this system can improve water use efficiency, reduce manual intervention, and provide more accurate crop yield predictions, thus supporting more productive and sustainable smart agriculture. This research evaluates the accuracy of soil sensors and a CNN model in agricultural monitoring, achieving high reliability with nutrient (99.9998%), pH (99.05440/0), and moisture (99.3827%) sensors, as well as fruit weight prediction (99.25% to 99.93%). These results confirm the system's suitability for precision agriculture, with ongoing developments to enhance the application interface. © 2024 IEEE.

关键词： Soil moisture

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Machine Learning and Deep Learning for Enhanced DDoS Detection in IoT-Based Intrusion Detection System 1

Machine Learning and Deep Learning for Enhanced DDoS Detecti...

引用

1st international conference on Innovative and Intelligent Information Technologies, IC3IT 2024

作者： Kharoubi, Kamir Cherbal, Sarra Akkal, Maroua Ferhat Abbas University Setif 1 Lrsd Lab Faculty of Sciences Setif Algeria

ISBN: (纸本)9798331541064

Securing internet of Things (IoT) networks is critical due to their increasing prevalence and susceptibility to cyberattacks. Machine Learning (ML) and Deep Learning (DL)powered Intrusion Detection Systems (IDS) provide a promising solution to counter these threats. This study focuses on detecting distributed Denial of Service (DDoS) attacks, ensuring IoT device availability and protecting critical services within IoT ecosystems. Five ML and DL models, including XGBoost, were trained and evaluated on the recent CICIoT2023 dataset to classify network traffic as benign or DDoS. Evaluation results show that XGBoost outperformed other models, achieving a 99. 96% accuracy in multi-classifying DDoS attack types with benign traffic and a rapid prediction time of 0.56 microseconds per traffic sample. These results highlight XGBoost's potential for real-time IoT security applications. © 2024 IEEE.

关键词： Network intrusion

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Secure and Lightweight Access Control for Highly Decentralized and distributed File Systems 1

Secure and Lightweight Access Control for Highly Decentraliz...

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1st international Workshop on Middleware for the computing Continuum, Mid4CC 2023

作者： Jo, Yeongbin Cho, Yunsang Kim, Hokeun Hanyang University Seoul Korea Republic of Arizona State University TempeAZ United States

ISBN: (纸本)9798400704574

The overall data traffic on the internet has been rapidly growing, leading to a significantly increased cost of storing the exploding data. Decentralized and distributed file systems such as InterPlanetary File System (IPFS) and BitTorrent have been increasingly widely used for storing and sharing various and massive data at a much lower cost. However, providing reliable access control to highly decentralized and distributed file systems for IoT networks is a challenging problem due to resource constraints, management overhead, and heterogeneity. To address this problem, we propose secure and lightweight access control for highly decentralized and distributed file systems. We design and implement a prototype of the proposed approach by extending an open-source software platform for access control in IoT called Secure Swarm Toolkit (Sst) and using IPFS with an additional file system management mechanism. Our evaluation of the proposed approach with embedded IoT devices shows promising results by providing access control for decentralized and distributed file systems at minimal computation and communication costs. We expect our proposed approach to be applied to secure decentralized data storage with much lower costs compared to existing access control algorithms for decentralized file systems primarily based on blockchain technologies. © 2023 ACM.

关键词： Access control

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Proactive Content Caching via Interplay Between Deep Learning and stochastic Optimization 21

Proactive Content Caching via Interplay Between Deep Learnin...

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21st IEEE international conference on Mobile Ad-Hoc and Smart Systems (MASS)

作者： Park, Yongmoon Lee, Kyungtae Choi, Minseok Kim, Yeongjin Inha Univ Incheon South Korea Kyung Hee Univ Yongin South Korea

ISBN: (纸本)9798350363999;9798350364002

The increasing demand for video data traffic, along with the proliferation of smart devices, poses significant challenges to content and internet service providers. In response to this challenge, content caching on mobile edge computing (MEC) servers has emerged to reduce content download latency. However, existing caching solutions often assume stationary content popularity or require real-time knowledge of popularity, which does not align with real-world scenarios. To address these limitations, we introduce ProCache, a novel content caching algorithm. ProCache takes into account spatial and temporal monetary budget sharing for caching, content sizes, and original server locations while dealing with uncertain regional content popularity. The goal of ProCache is to minimize the long-term expected content download latency overall active users, comprising two key components. First, the deep learning module includes the 1DCNN-LstM-Dense layered deep learning model for predicting future content requests and the data mapping module which makes the model focus more on the request trend rather than the magnitude. Second, based on our predictions, the stochastic optimization module runs a dynamic content caching algorithm based on the Lyapunov optimization technique that operates in a fully distributed manner by region and ensures overall performance bounds. Trace-driven simulations using the YouTube dataset demonstrate that ProCache outperforms existing prediction models and content caching algorithms.

关键词： Content Caching Deep learning Edge computing Proactive caching stochastic optimization

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Software Defined Network Level for Local Processing and Global Processing of IoT-Fog Network Events 1

Software Defined Network Level for Local Processing and Glob...

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1st international conference on Data, Computation and Communication, ICDCC 2024

作者： Rajput, Sarkarsinha H. Patil, Manoj E. SSBT's College of Engineering & Technology Department of Computer Engineering Bambhori (M.S.) Jalgaon India

ISBN: (纸本)9798331532956

A Software Defined Network (SDN) is a modern networking approach that makes it possible to directly program and manage network traffic, while also simplifying the underlying infrastructure for applications and services. It works by separating the decision-making (control plane) from the actual data transmission (data plane), giving greater flexibility and control over how networks operate. Fog computing architecture and internet of Things (IoT) devices are combined to create IoT-Fog Networks, which aim to solve the issues of latency, scalability, bandwidth limitations, and real-time data processing. The intermediary processing resources known as fog nodes are situated nearer the network's edge in fog computing. The majority of fog devices are not very computationally or memory-intensive. The task of skillfully handling these constraints in order to fulfill the requirements of multiple applications is difficult. Samples in the current IoT-Fog network are overly delayed in their communication or, on occasion, may be lost due to communication and processing overload due to congestion between devices and gateways. One way to address this is to provide a method that is effective for handling IoT-Fog Events in Software Defined Network (SDN), both locally and globally. © 2024 IEEE.

关键词： Fog computing internet of Things Software Defined Network

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